scholarly journals The Immune Epitope Database: How Data Are Entered and Retrieved

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Ward Fleri ◽  
Kerrie Vaughan ◽  
Nima Salimi ◽  
Randi Vita ◽  
Bjoern Peters ◽  
...  
Keyword(s):  
Experimental Data ◽  
Biomedical Research ◽  
Nonhuman Primates ◽  
Allergic Diseases ◽  
Research Community ◽  
Easy Access ◽  
Immune Epitope ◽  
Free Service ◽  
Epitope Discovery ◽  
Immune Epitope Database

Easy access to a vast collection of experimental data on immune epitopes can greatly facilitate the development of therapeutics and vaccines. The Immune Epitope Database and Analysis Resource (IEDB) was developed to provide such a resource as a free service to the biomedical research community. The IEDB contains epitope and assay information related to infectious diseases, autoimmune diseases, allergic diseases, and transplant/alloantigens for humans, nonhuman primates, mice, and any other species studied. It contains T cell, B cell, MHC binding, and MHC ligand elution experiments. Its data are curated primarily from the published literature and also include direct submissions from researchers involved in epitope discovery. This article describes the process of capturing data from these sources and how the information is organized in the IEDB data. Different approaches for querying the data are then presented, using the home page search interface and the various specialized search interfaces. Specific examples covering diverse applications of interest are given to highlight the power and functionality of the IEDB.

scholarly journals The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design

2017 ◽  
Vol 8 ◽  
Author(s):  
Ward Fleri ◽  
Sinu Paul ◽  
Sandeep Kumar Dhanda ◽  
Swapnil Mahajan ◽  
Xiaojun Xu ◽  
...  
Keyword(s):  
Vaccine Design ◽  
Immune Epitope ◽  
Epitope Discovery ◽  
Immune Epitope Database

scholarly journals An immunologically friendly classification of non-peptidic ligands

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Lindy Edwards ◽  
Rebecca Jackson ◽  
James A Overton ◽  
Randi Vita ◽  
Nina Blazeska ◽  
...  
Keyword(s):  
Experimental Data ◽  
Single Letter ◽  
Ncbi Taxonomy ◽  
Web Interface ◽  
Immune Epitope ◽  
Improve Access ◽  
Immune Epitope Database

Abstract The Immune Epitope Database (IEDB) freely provides experimental data regarding immune epitopes to the scientific public. The main users of the IEDB are immunologists who can easily use our web interface to search for peptidic epitopes via their simple single-letter codes. For example, ‘A’ stands for ‘alanine’. Similarly, users can easily navigate the IEDB’s simplified NCBI taxonomy hierarchy to locate proteins from specific organisms. However, some epitopes are non-peptidic, such as carbohydrates, lipids, chemicals and drugs, and it is more challenging to consistently name them and search upon, making access to their data more problematic for immunologists. Therefore, we set out to improve access to non-peptidic epitope data in the IEDB through the simplification of the non-peptidic hierarchy used in our search interfaces. Here, we present these efforts and their outcomes. Database URL:  http://www.iedb.org/

scholarly journals Epitope Specific Antibodies and T Cell Receptors in the Immune Epitope Database

2018 ◽  
Vol 9 ◽  
Author(s):  
Swapnil Mahajan ◽  
Randi Vita ◽  
Deborah Shackelford ◽  
Jerome Lane ◽  
Veronique Schulten ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptors ◽  
Specific Antibodies ◽  
Cell Receptors ◽  
Immune Epitope ◽  
Immune Epitope Database

scholarly journals Let’s do better: public representations of COVID-19 science

FACETS ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 403-423
Author(s):  
Timothy Caulfield ◽  
Tania Bubela ◽  
Jonathan Kimmelman ◽  
Vardit Ravitsky
Keyword(s):  
Biomedical Research ◽  
Public Trust ◽  
Research Process ◽  
Research Community ◽  
Public Communication ◽  
The Public ◽  
Research Communities ◽  
Key Issues ◽  
Bad Data

COVID science is being both done and circulated at a furious pace. While it is inspiring to see the research community responding so vigorously to the pandemic crisis, all this activity has also created a churning sea of bad data, conflicting results, and exaggerated headlines. With representations of science becoming increasingly polarized, twisted, and hyped, there is growing concern that the relevant science is being represented to the public in a manner that may cause confusion, inappropriate expectations, and the erosion of public trust. Here we explore some of the key issues associated with the representations of science in the context of the COVID-19 pandemic. Many of these issues are not new. But the COVID-19 pandemic has placed a spotlight on the biomedical research process and amplified the adverse ramifications of poor public communication. We need to do better. As such, we conclude with 10 recommendations aimed at key actors involved in the communication of COVID-19 science, including government, funders, universities, publishers, media, and the research communities.

scholarly journals Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19

2020 ◽  
Author(s):  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Neelam Sharma ◽  
Salman Sadullah Usmani ◽  
Gajendra P S Raghava
Keyword(s):  
Scientific Community ◽  
Prediction Models ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Independent Validation ◽  
Immune Epitope ◽  
Learning Techniques ◽  
Wide Range ◽  
Immune Epitope Database

Abstract Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/).

Training nonhuman primates to perform behaviors useful in biomedical research

Lab Animal ◽  
2005 ◽  
Vol 34 (5) ◽  
pp. 37-42 ◽  
Author(s):  
Steven J. Schapiro ◽  
Jaine E. Perlman ◽  
Erica Thiele ◽  
Susan Lambeth
Keyword(s):  
Biomedical Research ◽  
Nonhuman Primates
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